The brains of animals are formed of neurons interconnected into neural networks. A particular property of these neural networks is that they can be trained to have appropriate responses to various stimuli. In recent years, neurons are being cultivated outside the body and there is some desire to build computers based on or aided by biological neural networks, rather than on sequential electronic logic or artificial neural networks.
To date, however, no significant controlled training of neurons has been achieved. “Natural” in-vivo training of neurons, for example, teaching people to play the piano, has been achieved, of course. In some experiments, the stimulation signals are provided directly to neurons in the brain, rather than to sensory neurons. However, even such training does not allow any artificial control of the training process. What has been achieved artificially is causing two neurons to be synchronized in their behavior, by stimulating both the neurons simultaneously, several times.
The current theory for explaining neural learning, for example described in Schultz W., “Predictive Reward Signal of Dopamine Neurons”, in J. Neurophysiol. 80:1-27 (1998), Schultz W and Dickinson A, “Neural Coding of prediction Errors”, in Annu. Rev. Neurosci. 23:473-500 (2000), Spangel R and Weiss F, “The dopamine Hypothesis of Reward: Past and Current Status:, in Trends Neurosci, 22:521-527 (1999), Gisiger T, Dehaen S and Changeux J P, “Computational Models of Associative Cortex”, Curr Opin Neurobiol, 10:250-259 (2000) and Kalivas P W and Nakamura M, “Neural Systems for Behavioral Activation and Reward”, Curr Opin Neurobiol, 9:223-227 (1999), the disclosures of which are incorporated herein by reference, postulate a rewarding “circuit” that generates a signal that acts as a reward when a salient event occurs or a goal is achieved, which signal causes the neural network to retain or change the last applied response, as the proper, learned response to an input stimulus.
Other theories have been proposed for learning on a behavioral level. For example, C. Hull, “Principles of Behavior” Appleton-Century-Crofts, New-York (1943) and E. R. Guthrie, “Psychological Facts and Psychological Theory”, in Psychological Bulletin 43 (1946) [Presidential address of the APA, Evanston, Ill. (1945)], the disclosures of which are incorporated herein by reference, suggest that when a goal is achieved, a resulting reward acts to reduce the driving stimuli of the learning process. There is no clear mechanism connecting cognitive theories and neuronal theories of learning or activity.
The functional structure of the brain includes multiple functional areas, some of which (e.g., the motor and sensory portions) are arranged in a generally hierarchical manner, in that a higher level area uses input from a lower level area or sends commands to a lower level area. A general mapping of brain areas to key functions is known. In addition, various methods of stimulating living brains and detecting activity in the brain are known as well.
J Wessberg, C R Stambaugh, J D Kralik, P D Beck, M Laubach, J K Chapin, J Kim, S J Biggs M A Srinivasan and M A L Nicolelis, “Real-Time Prediction of hand Trajectory by Ensembles of cortical Neurons in Primates” in Nature 408:6810 (2000) p 361, the disclosure of which is incorporated herein by reference, is exemplary of several attempts to interface actuators to living neural networks. In this paper, an animal is trained to control a robotic arm using its brain's motor center, via electrodes attached to areas in the motor center associated with the desired movement. This paper also suggests using an electrode array applied to a brain, to detect activation patterns that correspond to various actions. Thus, the brain can effect an action, for example using a prosthetic attachment, when the brain “thinks” the patterns, and the patterns are detected by the electrode array.
D V Buonomano and M M Merzenich in “Cortical Plasticity: From Synapses to Maps”, Annu. Rev. Neurosci. 21:149-186 (1998), the disclosure of which is incorporated herein by reference, describes various experiments performed to study learning in the brain. In particular, on page 160 an experiment showing in-vivo pairing of vision neurons with external stimuli is described. The correlation between the activity of these neurons and the occurrence of an event in the visual field was modified, by stimulating the neurons electrically when the event occurs.
This paper also discusses the plasticity of the cortex, for example, in receiving inputs from local or remote areas and in changing the function and/or location of a function, in response to training.
Learning by humans, and animals as far as is known, is generally achieved through the use of motivation. In a typical learning situation, an animal or human is rewarded for good results and/or punished for bad results. The reward may have various forms, including immediate reward and punishment, and, in humans, delayed reward and punishment, which, however, is converted into an immediate reward or punishment by the actions of the human consciousness.
One difficulty in training animals comes from the lack of a delayed reward/punishment mechanism. Another difficulty is that it is difficult, if not impossible, to provide an animal with exact feedback and/or pointers to what exact item it did not learn right, so that training can focus on those items. With a human, both of these are possible, albeit, sometimes frustrating.
Various neural stimulators are known in the art, for example, U.S. Pat. Nos. 6,341,236, 6,066,163 and 5,522,863 and US patent application publication US 2002/002390 A1, the disclosures of which are incorporated herein by reference. Various circuitry, software and other components and/or parameter settings described in these patents may find use in some embodiments of the present invention, for example as set out below.